Introduction

The corpus that is used for this project is the top 50 of seven different countries on Spotify. The specific countries that were selected are: Australia, Brazil, Morocco, the Netherlands, the Philippines, the United Kingdom and the United States. This list of countries was chosen because it contains the most popular top 50 list by number of likes on Spotify of each continent. The Netherlands was added to these most popular top 50 lists because it’s especially interesting to me as someone who lives in the Netherlands. This corpus was chosen to have both a manageable amount of songs, but also represent music from all over the world while being representative of a large chunk of the listeners of each continent.

One limitation of this corpus is the fact that the countries that are the most popular in each continent are suspiciously often English-speaking countries which means that English music might be over-represented when compared to the rest of the countries that are not concluded. It also might mean that the playlists that have the most likes are not from the country with the most Spotify listeners on a continent, but that a lot of people from across the world like the top 50 of this country. This could be because English is a very commonly spoken language everywhere. However, this is hard to prove, so the reasonable assumption is that the most-liked songs are from the countries that listen to Spotify the most.

Despite these limitations this website will attempt to find both regional differences and universal trends in popular music by comparing the different top 50 lists. It will also highlight some interesting outliers that are different from the vast majority of popular songs in this large corpus and dive into what makes these outliers popular despite them being different. By doing all this I hope to find out what “ingredients” a popular song is made up of in general and what “ingredients” of popular music are region-dependent, and also when these “ingredients” can be ignored or changed.

Important note: Due to the playlists changing daily, the descriptions of the graphs about the playlists are very likely not completely accurate when viewing the page. But they were completely accurate at the time of writing, hopefully the insights discussed are accurate so that they stay relevant.

Release date of the songs

Acousticness

Acousticness shows an interesting trend, it seems like all the English-speaking countries and the Netherlands prefer less acoustic songs, Morocco likes acoustic music the most by far and their regional music seems to have more acoustic elements which reflects that preference.

Danceability

It seems like there are no strong regional trends in danceability between countries, Morocco has a slightly higher than average danceability preference and Philippines a slightly below average preference for danceability.

Energy

Brazil, the Netherlands and the UK prefer music with more energy, which might mean that South-America and Europe in general like more energetic music. The Philippines is far below average in the energy level of their music, this seems to be a strong regional preference.

Liveness

It seems like live music is not really all that popular in any country, on average very popular music is nearly always very polished and thus not live. Only Brazil seems to have a significant number of tracks that have a higher value in liveness, which says something about the type of music they prefer. Brazil seems to care less about the polish of typical pop music and they may appreciate the more “real” live sound.

Loudness

This graph is very similar to the energy graph, with the same countries on top and at the bottom. One major difference that can be found is that Brazil has a way higher average than second place, in energy the difference was smaller. This again seems to be regional preference.

Speechiness

This graph is very similar to liveness, popular music usually has little to no speech since that is a feature that just doesn’t occur in very popular songs in general. But again there are exceptions, Morocco and Brazil do seem to have a significant amount off tracks that have some amount of speechiness which says something about the regional music they prefer.

Tempo

It’s very interesting to see that there really seems to be an optimal tempo for a song to become popular regardless of region, only in Brazil is the average preferred tempo a bit higher which makes sense when considering their preferrence for louder and more energetic music than average.

Valence

The average valence seems to be at or slightly below zero, which is surprising to me because you would expect popular music to be happier on average since people like music that makes them happy in general. However, Brazil and Morocco break this trend showing a clear preference for happier songs.

Chroma analysis of an outlier

This graph shows the chroma analysis of the oldest song in the corpus: “Iris” by Goo Goo Dolls. This song clearly differs from the norm in one way by being way older than the average song in the corpus. However, is it also different when looking at the chroma analysis of the song? The answer is no, but that does not mean that this chroma analysis is not interesting at all. It’s actually a good representation of the typical popular song. After looking at the chroma analysis of a big sample of the corpus I can conclude that this kind of chroma analysis is typical. Very few songs modulate at all, and most songs have a very simple chroma analysis like this one. This is something that is actually very noticeable when you start paying attention to it specifically when listening to popular songs. The biggest change that occurs in the song in the graph below is the lack of activity in A replaced by activity in F sharp during the instrumental section of the song highlighted using the red lines.

Chroma analysis of an outlier

Structure similarity matrix of an outlier

The structure of “Europapa”

These are two self similarity matrices of “Europapa” by Joost. The self similarity matrices don’t really help answer the research question, because they show that the timbre changes often in some songs and less in others. A lot of songs have a clear repeating chorus and a lot don’t, the number of times the chorus repeats clearly also differs. So there aren’t really a lot of interesting trends to be found here. However, this graph does show some interesting things about “Europapa” particularly, it clearly shows the intro as being significantly different in timbre, but similar in chroma. The graph does have difficulty showing the chorus for this song, the first chorus can be seen in the chroma graph (starts at 13 seconds) but not in the timbre graph due to the instrumental changing for the second and third chorus. The timbre graph does show the second and third chorus clearly at 49 seconds and at 96 seconds since they have the same instrumental. This third chorus is less clear in the chroma graph but still there. The graph also clearly shows the start of the buildup to the drop of the song at around 120 seconds when both the chroma and the timbre change very significantly. So overall it’s interesting how these self similarity matrices are capable of capturing a lot of the most important musical features of the song, but still can have difficulty showing something very fundamental like the chorus.

Chordogram of a typical pop song

This chordogram shows the chords in “End of Beginning” by Djo, this song shows six clear sections. This is a typical feature of many popular songs in the corpus and many chordograms of pop songs look very similar to this. This is because the sections showing the same chords are the chorus being repeated three times with the same instrumental which has historically been a key feature in creating many popular and most importantly, catchy songs. The reason that this chordogram is so clear is due to the very abrupt nature of the changes in this song. Many other popular songs have chordograms that are similar, but the changes in these chordograms are often less obvious. This is why this particular song was chosen to show this general trend of of popular songs.

Chordogram

Tempogram

Not all pop is rhythmically simple

This tempogram shows an outlier from the corpus. The tempogram is of the song “Pink + White” by Frank Ocean, which is not only one of the older songs in the corpus but also one of the very few songs that do not have a clear unchanging tempo all the way through the song. Overall Spotify estimates the tempo of this track at 160 BPM, and most online sources agree with this assessment, however the graph also shows activity at multiple other BPM values. This is really interesting because it suggests that the tempo of the song might not be clear to the listener, which, looking at the other song in the corpus, is not a recipe for a successful song. However, “Pink + White” has clearly defied the odds and has been a massively successful regardless, even charting again years after its initial release date.

So what did I find?

In conclusion this website attempted to find regional trends and general trends in popular music, while also highlighting some interesting outliers. It does this based on the top 50 of Australia, Brazil, Morocco, the Netherlands, the Philippines, the United Kingdom and the United States. Let’s take a look at some features that of these countries.

English-speaking countries (Australia, USA and the UK) and the Netherlands

• Generally very similar and very close to average on most metrics.

• The UK prefers older songs more, with the oldest song having been released in 1998.

• USA prefers songs from between 2014 and 2022 more than average.

Brazil

• Prefers energetic songs

• Prefers way more live songs than the other countries.

• Prefers loud songs

• Likes songs that have some spoken parts.

• Prefers happier songs

Morocco

• Prefers acoustic songs.

• Prefers danceable songs slightly more.

• Likes songs that have some spoken parts.

• Prefers happier songs.

The Philippines

• Prefers older songs.

• Prefers songs with slightly less danceability.

• Prefers music with lower energy.

I also observed some general trends that are found across popular music. First of all, most popular songs are newer, but a significant number are older with the exact number depending on the region. Additionally, popular songs always have either an energy level above 0.4 or a valence level below ~0.5. I observed that very few popular songs modulate, most popular songs have similar chordograms with three repeats of the chorus and many popular songs have a very simple and unchanging tempogram. Every popular song can be divided up into either a more energetic and louder song or less energetic and quieter, the majority of songs fall into the first category.

Lastly I discussed some outliers. First of all, a lot of older songs get popular due to TikTok because they evoke nostalgia and have short recognizable soundbytes. Popular songs with a low valence level but a high energy level are really rare and those that exist often seem like happy songs at first, but discuss sad themes in their lyrics. Songs with both low valence and high energy are often either also big on TikTok or people seem to simply connect with these songs due to their relatable themes. Lastly, while most songs are rhythmically simple and have an easily identifiable tempo, some, like Frank Ocean’s “Pink + White”, are harder to identify for Spotify.

So overall the website managed to answer the main research questions pretty successfully and show some valuable insights.